Libraries

suppressMessages(library(ggplot2))

## Warning: package 'ggplot2' was built under R version 3.6.2

library(data.table)
suppressMessages(library(dplyr))

## Warning: package 'dplyr' was built under R version 3.6.2

suppressMessages(library(tidyverse))

## Warning: package 'tidyverse' was built under R version 3.6.2

## Warning: package 'tibble' was built under R version 3.6.2

## Warning: package 'tidyr' was built under R version 3.6.2

## Warning: package 'readr' was built under R version 3.6.2

## Warning: package 'purrr' was built under R version 3.6.2

## Warning: package 'forcats' was built under R version 3.6.2

suppressMessages(library(TSclust))

## Warning: package 'TSclust' was built under R version 3.6.2

library(lmodel2)
library(ggh4x)

## Warning: package 'ggh4x' was built under R version 3.6.2

setwd("~/Desktop/Wunderlichlab/BLI/Files_for_submission/Supplement/") ##change as necessary

Determining ilux CFUs/µl at OD1

Figure S1

dpdata<-read.table("Data_table_s1.txt", header = T)

#Working with Mean CFUs across replicates ( just the SPot Measurements)
dpdata$cfumean <-rowMeans(dpdata[,8:13])
#CFUs/µl for spot measurements are calculated as (mean CFUs * Dilution factor) / Plated volume
dpdata$preCFUpul<-(dpdata$cfumean*dpdata$DF)/dpdata$CFUvolume

dpdata$Plate<-as.numeric(as.character(dpdata$Plate))

## Warning: NAs introduced by coercion

#CFUs/µl for plate measurements are calculated as (mean CFUs * Dilution factor) / Plarted volume
dpdata$plateCFUpul<-(dpdata$Plate*dpdata$DF)/dpdata$PlateVolume

## add date to the DF
temp1<-dpdata[,c("Date","preCFUpul")]
temp1$Treatment<-"preCFUpul"
colnames(temp1)[2]<-"CFUµl"
temp2<-dpdata[,c("Date","plateCFUpul")]
temp2$Treatment<-"plateCFUpul"
colnames(temp2)[2]<-"CFUµl"
dpdatamelt<-rbind(temp1,temp2)

dpdatamelt<-dpdatamelt[is.na(dpdatamelt$CFUµl)==FALSE & dpdatamelt$CFUµl != 0 ,]
testmeans <- aggregate(CFUµl ~  Treatment, dpdatamelt, mean)
paste0("Average CFUs/µl atr OD 1 (Plate counts) : ", testmeans[1,2])

## [1] "Average CFUs/µl atr OD 1 (Plate counts) : 546666.666666667"

paste0("Average CFUs/µl atr OD 1 (Spot counts) : ", testmeans[2,2])

## [1] "Average CFUs/µl atr OD 1 (Spot counts) : 403508.771929825"

paste0("Average CFUs/µl atr OD 1 (Combined Measurements) : ",mean(testmeans$CFUµl))

## [1] "Average CFUs/µl atr OD 1 (Combined Measurements) : 475087.719298246"

FIG_S1<-ggplot(data = dpdatamelt, aes(x=`Treatment`,y=`CFUµl`))

FIG_S1 +
  geom_boxplot() +
  ylab("CFUs/µl")+
  ggtitle("Supplemental Figure 1\nCalculated CFUs/µl OD600 = 1 ")+
  #scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  geom_jitter(size = 5,shape=16, position=position_jitter(0.2))+
  stat_summary(fun.y=mean, geom="point", shape=20, size=5, color="red", fill="red")+
  geom_text(data = testmeans, aes(label = CFUµl, y = CFUµl + 0.08))+
  scale_x_discrete(labels=c("preCFUpul" = "Spot Measurment\n5µl", "plateCFUpul" = "Plate Measurment\n90µl"))

## Warning: `fun.y` is deprecated. Use `fun` instead.

characterizing the relationship between radiance and CFUs

Looking in liquid culture

Figure 2A

ex1_data<-read.table("Data_table_s2.txt", header = T)
#lets get the data into a workable format: 
ex1_data_re<-gather(ex1_data, "cfu", "radiance", X301840491:X47)
c_r<-as.data.frame(t(as.data.frame(strsplit(ex1_data_re$cfu,"X"))))
ex1_data_re<-cbind(ex1_data_re, c_r$V2)
##rename our cfus per µl column
colnames(ex1_data_re)[5]<-"cfupµl"
rownames(ex1_data_re)<-seq(1,nrow(ex1_data_re),1)
#we should also probably do a flux/cfu huh
ex1_data_re$t_CFU<-as.numeric(as.character(ex1_data_re$cfupµl))
ex1_data_re$RpCFU<-(as.numeric(as.character(ex1_data_re$radiance)))/ex1_data_re$t_CF
##remove NA
ex1_data_re<-ex1_data_re[complete.cases(ex1_data_re),]
ex1_data_re$log_t_CFU<-log(ex1_data_re$t_CFU)
mean_rpcfu<-mean(ex1_data_re[ex1_data_re$t_CFU>=3600,colnames(ex1_data_re)=="RpCFU"])
ex1_data_re$log_radiance<-log(ex1_data_re$radiance )

## Warning in log(ex1_data_re$radiance): NaNs produced

ax_breaks<-c(1,10,100,1000,10000,100000,1000000,10000000,100000000,1000000000)
ax_labeles<-c("1","10","100",expression("10"^3),expression("10"^4),expression("10"^5),expression("10"^6),expression("10"^7),expression("10"^8),expression("10"^9))

ex1_data_sub<-ex1_data_re[ex1_data_re$t_CFU>=1000 & ex1_data_re$radiance >=1000,]

ggplot_regression<-lmodel2(ex1_data_sub$log_radiance~ex1_data_sub$log_t_CFU, data=ex1_data_sub ,nperm = 2)

## RMA was not requested: it will not be computed.

colorcodes<-c("#070504","#070504","#070504","#070504","#070504","#070504","#F58B80","#C6AE2E","#30B44A","#2BBFC7","#81ABDA","#D988BA")

ex1_data_re$cfu<-as.factor(ex1_data_re$cfu)
levels(ex1_data_re$cfu)<-c("0.00003","0.00000375","0.00006","0.0006","0.006","0.6","6","0.06","0.0000075","9.375E-07","0.000015","0.000001875")
ex1_data_re$cfu<-ordered(ex1_data_re$cfu, levels=c("9.375E-07","0.000001875","0.00000375","0.0000075","0.000015","0.00003","0.00006","0.0006","0.006","0.06","0.6","6"))

f2a<-c("0.00003","0.00006","0.0006","0.006","0.06","0.6","6")
f2acolors<-c("#070504","#F58B80","#C6AE2E","#30B44A","#2BBFC7","#81ABDA","#D988BA")

FIG2A<-ggplot(data = ex1_data_re[ex1_data_re$cfu %in% f2a , ], aes(x=`cfu` ,y=`radiance`, color=`cfu`)) 
##save at 3.5 x 3.5
FIG2A +
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 5, alpha = .5) +
  ylab("total flux\n (p/s)")+
  xlab("OD")+
  #geom_abline(slope = f2areg$coef[[2]], intercept = f2areg$coef[[1]])+
  ggtitle("Figure2A\ntotal fluxvs OD in liquid culture")+
  scale_color_manual(values = f2acolors)+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw() + 
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10, angle = 90), axis.text.y = element_text(size= 10),
        legend.position = "none")

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning in self$trans$transform(x): NaNs produced

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 2 rows containing missing values (geom_point).

looking in injected flies

Figure S3

leastdilute<-function(fulldata){
  tempmatrix<-data.frame(matrix(ncol = length(fulldata), nrow = 0))
  for ( i in 1:length(unique(fulldata$Fly))){
    tempdata<-fulldata[fulldata$Fly==i,]
    tempdata<-tempdata[1,]
    tempmatrix<-rbind(tempmatrix,tempdata)
    rm(tempdata)
  }
  return(tempmatrix)
}

#males and females
ex3b_dp<-read.table("Data_table_s3.txt", header = T)
ex3b_dp<-ex3b_dp[complete.cases(ex3b_dp),]
ex3b_flux<-read.table("Data_table_s4.txt", header = T)

ex3b_dp$cfupfly<-rowMeans(ex3b_dp[,c("A","B","C")], na.rm = TRUE)*ex3b_dp$DF*250

ex3b_hidp<-leastdilute(ex3b_dp)
ex3b_complete<-merge(ex3b_flux, ex3b_hidp, all=T, by.x=6, by.y=7 )
ex3b_complete$log_Flux<-log(ex3b_complete$Flux)
ex3b_complete$log_cfupfly<-log(ex3b_complete$cfupfly)
ex3b_complete<-ex3b_complete[ex3b_complete$log_cfupfly!="-Inf",]
ex3b_female<-ex3b_complete[ex3b_complete$Sex.x=="f",]

freg<-lmodel2(ex3b_female$Flux~ex3b_female$cfupfly , data=ex3b_female , nperm = 99)

## RMA was not requested: it will not be computed.

freglog<-lmodel2(ex3b_female$log_Flux~ex3b_female$log_cfupfly , data=ex3b_female , nperm = 99)

## RMA was not requested: it will not be computed.

ex3b_male<-ex3b_complete[ex3b_complete$Sex.x=="m",]
mreg<-lmodel2(ex3b_male$Flux~ex3b_male$cfupfly , data=ex3b_male , nperm = 99)

## RMA was not requested: it will not be computed.

mreglog<-lmodel2(ex3b_male$log_Flux~ex3b_male$log_cfupfly , data=ex3b_male, nperm = 99 )

## RMA was not requested: it will not be computed.

m_CI<-abs(mreglog[[3]][3,3]-mreglog[[4]][3,4])
paste("male slope CI +/- ",m_CI)

## [1] "male slope CI +/-  0.102729747416191"

f_CI<-abs(freglog[[3]][3,3]-freglog[[4]][3,4])
paste("female slope CI +/- ",m_CI)

## [1] "female slope CI +/-  0.102729747416191"

FIG_S3<-ggplot(data = ex3b_complete, aes(x=`cfupfly` ,y=`Flux`, color=`Sex.y`))
FIG_S3+
  annotate("rect", xmin = 0, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 5, alpha = .5)+
  ylab("total flux\n (p/s)")+
  xlab("CFUs/fly")+
  ggtitle("Supplemental Figure 3\nTotal total flux vs Total CFUs ")+
  geom_abline(slope = mreglog[[3]][3,3], intercept = mreglog[[3]][3,2], color="#00BFC4" )+
  geom_abline(slope = freglog[[3]][3,3], intercept = freglog[[3]][3,2], color="#F8766D")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  scale_x_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw() +
  scale_color_discrete(name="sex")+
  annotate("text", x=100000, y=40000000, label= paste("Male: y=",signif(mreglog[[3]][3,3],2),"x",signif(mreglog[[3]][3,2],2)))+
annotate("text", x=1500, y=40000000, label= paste("Female: y=",signif(freglog[[3]][3,3],2),"x+",signif(freglog[[3]][3,2],2)))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Transformation introduced infinite values in continuous x-axis

#### Figure 2B

#all males
ex3a_dp<-read.table("Data_table_s5.txt", header = T)
ex3a_dp<-ex3a_dp[complete.cases(ex3a_dp),]
ex3a_flux<-read.table("Data_table_s6.txt", header = T)
#first we calculate the predicted CFUs/fly
ex3a_dp$cfupfly<-rowMeans(ex3a_dp[,c("A","B","C")], na.rm = TRUE)*ex3a_dp$DF*250
##for now lets just pull out the least diluted calculation.
ex3a_hidp<-leastdilute(ex3a_dp)
ex3a_complete<-merge(ex3a_flux, ex3a_hidp, all=T, by.x=1, by.y=5 )
ex3a_complete$log_Flux<-log(ex3a_complete$Flux)
ex3a_complete$log_cfupfly<-log(ex3a_complete$cfupfly)
ggplot_regression3<-lmodel2(ex3a_complete$log_Flux~ex3a_complete$log_cfupfly , data=ex3a_complete , nperm = 99)

## RMA was not requested: it will not be computed.

regression_3b<-lmodel2(Flux~cfupfly , data=ex3a_complete, nperm = 99 )

## RMA was not requested: it will not be computed.

#Combining data for a mega regression 
temp_3a<-ex3a_complete[,c("Fly", "OD.x", "Flux", "cfupfly")]
temp_3a$Date<-20210301
temp_3b<-ex3b_complete[,c("Fly", "OD.x", "Flux", "cfupfly")]
temp_3b$Date<-20210402
ex3_mega<-rbind(temp_3a,temp_3b)
mega_regression<-lmodel2(log(Flux)~log(cfupfly) , data=ex3_mega , nperm = 99)

## RMA was not requested: it will not be computed.

paste("y=",mega_regression[[3]][3,3],"x+",mega_regression[[3]][3,2])

## [1] "y= 1.22289674222623 x+ 0.356592910721384"

mega_CI<-abs(mega_regression[[3]][3,3]-mega_regression[[4]][3,4])
paste("male slope CI +/- ",mega_CI)

## [1] "male slope CI +/-  0.0607450654754711"

colorcodes<-c("#30B44A","#2BBFC7","#81ABDA","#D988BA")
FIG2B<-ggplot(data = ex3_mega, aes(x=`cfupfly` ,y=`Flux`, color=as.factor(`OD.x`)))
#save pdf 4.5x3.5
FIG2B+
  geom_point(size = 4, alpha = .5)+
  ylab("total flux\n (p/s)")+
  xlab("CFUs/fly")+
  labs(col="OD")+
  ggtitle("Figure2B\ntotal fluxvs CFUs/fly ")+
  #geom_smooth(color="black" , method = "lm", formula = y~(x), fullrange = TRUE)+
  scale_color_manual(values = colorcodes)+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  scale_x_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw() +
  geom_abline(slope = mega_regression[[3]][3,3], intercept = mega_regression[[3]][3,2])+
  annotate("text", x=10000, y=100000000, label= paste("y=",signif(mega_regression[[3]][3,3],2),"x + ",signif(mega_regression[[3]][3,2],2)))+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10),
        legend.text=element_text(size=10),legend.title=element_text(size=10))

Looking at our ability to distinguish/track infections over time

Figure 3b

ex4_data<-read.table("Data_table_s7.txt", header = T)



ex4_data_re<-gather(ex4_data, "FlyID", "radiance", Fly1:Fly12)
ex4_data_re<-ex4_data_re[complete.cases(ex4_data_re),]
ex4_data_re$ID<-paste(ex4_data_re$experiment,ex4_data_re$FlyID, sep="")

fig3b<-ex4_data_re[ex4_data_re$experiment=="b",]
#Filter out lowest 2 ODs
fig3b<-fig3b[fig3b$p_CFU>70,]
fig3b$OD<-as.factor(fig3b$p_CFU)
levels(fig3b$OD)<-c( "0.006","0.06","0.6","6")

colorcodes3b<-c("#00BA38","#00BFC4","#619CFF","#F564E3")


FIG3B<-ggplot(data = fig3b, aes(x=`Day`,y=`radiance`, colour=as.factor(`OD`)))
##save pdf 4 x 3.5
FIG3B +
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 2, alpha = .5, position=position_jitter(0.1)) +
  ylab("total flux\n (p/s)")+
  xlab("day")+
  ggtitle("Figure3B\n")+
  geom_smooth()+
  labs(col="OD")+
  scale_color_manual(values = colorcodes3b)+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10),
        legend.text=element_text(size=10),legend.title=element_text(size=10))

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning in self$trans$transform(x): NaNs produced

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning in self$trans$transform(x): NaNs produced

## Warning: Transformation introduced infinite values in continuous y-axis

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 7 rows containing non-finite values (stat_smooth).

## Warning: Removed 7 rows containing missing values (geom_point).

Figure 3c

fig3c<-fig3b
colorcodes3c<-c("#00BA38","#00BFC4","#619CFF","#F564E3")

FIG3C<-ggplot(data = fig3c, aes(x=`Day`,y=`radiance`, colour=`OD`, group=`ID`))
#Individual tracks
FIG3C +
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 1, alpha = .5) +
  ggtitle("Figure 3C")+
  ylab("total flux\n (p/s)")+
  xlab("day")+
  facet_wrap(. ~ OD,ncol=4)+
  geom_path()+
  stat_smooth(aes(group = 1), color="black", size=.5,linetype="dashed")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw()+
  scale_colour_manual(values=colorcodes3c)+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10),
        legend.position="none", panel.grid.minor = element_blank())

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning in self$trans$transform(x): NaNs produced

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning in self$trans$transform(x): NaNs produced

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning in self$trans$transform(x): NaNs produced

## Warning: Transformation introduced infinite values in continuous y-axis

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 7 rows containing non-finite values (stat_smooth).

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 1 row(s) containing missing values (geom_path).

Figure S5

#line fit to data from ex1_data_sub
paste("Liquid Culture : y=",signif(ggplot_regression[[3]][3,3],2), signif(ggplot_regression[[3]][3,2],2), sep=" ")

## [1] "Liquid Culture : y= 1.2 -3.7"

# line fit to data from fig3b
paste("In Fly : y=",signif(mega_regression[[3]][3,3],2),signif(mega_regression[[3]][3,2],2) , sep=" ")

## [1] "In Fly : y= 1.2 0.36"

fun.liquid <- function(x) (ggplot_regression[[3]][3,3]*x) + ggplot_regression[[3]][3,2] 
fun.fly <- function(x) (mega_regression[[3]][3,3]*x) + mega_regression[[3]][3,2]

p <- ggplot(data = data.frame(x = 0), mapping = aes(x = x))
p + stat_function(fun = fun.liquid, colour = "red") + 
  stat_function(fun = fun.fly,colour = "blue") + 
  labs(title="Supplemental Figure 5\nPredicted Total Flux for Bacteria\n Solution vs Injected ", x="log10 CFUs",y="log10 total flux")+
  xlim(0,50)+
  ylim(0,50)+
  theme_bw()+
   annotate("text", x=35, y=10, label=paste("total flux in liquid culture (red)\n y=",signif(ggplot_regression[[3]][3,3],2), signif(ggplot_regression[[3]][3,2],2), sep=" "))+
   annotate("text", x=10, y=40, label=paste("total flux in fly (blue)\n y=",signif(mega_regression[[3]][3,3],2),"+",signif(mega_regression[[3]][3,2],2), sep=" "))

## Warning: Removed 18 row(s) containing missing values (geom_path).

## Warning: Removed 19 row(s) containing missing values (geom_path).

#### Figure s6

ex2b_data<-read.table("Data_table_s8.txt", header = T)
#Try3
ex2c_data<-read.table("Data_table_s9.txt", header = T)

#try2 data processing 
ex2b_data$ex<-"B"
ex2b_data$plost<-ex2b_data$negative/ex2b_data$Counts
ex2b_data<-ex2b_data[is.nan(ex2b_data$plost)==F,]
#filter out day1 df 100 ( its too dense) 
ex2b_data<-ex2b_data[!(ex2b_data$day==1 & ex2b_data$Dilution==100),]
#Following 5 lines removed since we now filter out plates with less than 
ex2b_datafilt<-ex2b_data[ex2b_data$Counts >= 30, ]

#try3 data processing 
ex2c_data$ex<-"C"
ex2c_data$plost<-ex2c_data$negative/ex2c_data$Counts
ex2b_data<-ex2b_data[is.nan(ex2b_data$plost)==F,]
#No additional filtering done Since only plates showing more than 30 colonies where imaged

##cobineB and C data
ex2_dataFULL<-rbind(ex2b_datafilt, ex2c_data)
testmeans <- aggregate(plost ~  day, ex2_dataFULL, mean)
testmeans$plost<-round(testmeans$plost,digits = 3)

FIG_S6AB<-ggplot(data = ex2_dataFULL, aes(x=as.factor(`day`) ,y=`plost`))

FIG_S6AB+ 
  geom_boxplot() +
  theme_bw() + 
  labs(title="Supplemental Figure 6A\nPlasmid loss over time",
       x="day",
       y= "proportion of colonies\nplasmid lost",
       caption = "Boxplot Shows Median red Dot is mean ")+
  ylim(0,.17)+
  geom_text(data = testmeans, aes(label = plost, y = plost+0.01))+
  stat_summary(fun=mean, geom="point", shape=20, size=5, color="red", fill="red")+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10),
        legend.text=element_text(size=10),legend.title=element_text(size=10))

FIG_S6AB+ 
  geom_boxplot() +
  theme_bw() + 
  labs(title="Supplemental Figure 6B\nPlasmid loss over time\nfly feeding conditions",
       x="day",
       y= "proportion of colonies\nplasmid lost",
       caption = "Boxplot Shows Median red Dot is mean ")+
  ylim(0,.17)+
  geom_text(data = testmeans, aes(label = plost, y = plost+0.01))+
  geom_point(position=position_jitterdodge(jitter.width=0, dodge.width = 0.3, seed = 1234), size = 5,shape=16, alpha = .5, aes(color=Condition)) +
  stat_summary(fun=mean, geom="point", shape=20, size=5, color="red", fill="red")+
  #scale_y_continuous(trans="log10")+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10),
        legend.text=element_text(size=10),legend.title=element_text(size=10))

FIG_S6C<-ggplot(data = ex2_dataFULL, aes(x=as.factor(`day`) ,y=`negative`))
FIG_S6C+ 
  geom_boxplot()+ 
  xlab("day")+
  ylab("total of colonies\nplasmid lost")+
  ggtitle("Supplemental Figure 6C\nPlasmid loss over time") +
  theme_bw() + 
  geom_point(size = 5,shape=16, alpha = .5)+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10),
        legend.text=element_text(size=10),legend.title=element_text(size=10))

Looking at our ability to track different types for infections

Figure 4b

ex4c_cfu<-read.table("Data_table_s10.txt", header = T)
ex4c_death<-read.table("Data_table_s11.txt", header = T)
#reshape it!
ex4c_cfu<-gather(ex4c_cfu, "FlyID", "radiance", Fly1:Fly12)
ex4c_death<-gather(ex4c_death, "FlyID", "Death", Fly1:Fly12)
ex4c_death<-ex4c_death[ex4c_death$Death==T,]
ex4c_cfu$ID<-paste(ex4c_cfu$Well,ex4c_cfu$FlyID, sep="")
##give ach fly a unique identifier
genotypenamer<-function(fulldata){
  tempgeno<-character()
  for (i in 1:nrow(fulldata)){
    if (fulldata[i,c("Well")]=="B"){
      tempgeno<-append(tempgeno,"G090")
    } else if (fulldata[i,c("Well")]=="G"){
      tempgeno<-append(tempgeno,"G130")
    } else{
      print("well name error")
    }
  }
  return(tempgeno)
}

genotype<-genotypenamer(ex4c_cfu)
ex4c_cfu<-cbind(ex4c_cfu,genotype)
### label means for each condition
ex4c_means<-c(mean(ex4c_cfu[ex4c_cfu$genotype=="G090" & ex4c_cfu$Hour==0,c("radiance")]),mean(ex4c_cfu[ex4c_cfu$genotype=="G130" & ex4c_cfu$Hour==0,c("radiance")]))
# Now we label things
above_average<-function(fulldata, datameans){
  above_ave<-character()
  time0<-fulldata[fulldata$Hour==0,]
  for ( i in 1:nrow(time0)){
  if (time0[i,c("genotype")]=="G090"){
    if (time0[i,c("radiance")]>= datameans[1]){
      above_ave<-append(above_ave,time0[i,c("ID")])
    } else{
      next
    }
  } else {
    if (time0[i,c("radiance")]> datameans[2]){
      above_ave<-append(above_ave,time0[i,c("ID")])
    } else{
      next
    }
  }
  }
  aalabs<-fulldata$ID %in% above_ave
  return(aalabs)
}


above_ave<-above_average(ex4c_cfu,ex4c_means)
ex4c_cfu<-cbind(ex4c_cfu, above_ave)
##
deathdefiner<-function(fulldata,deaths){
  tempdeath<-logical(length=nrow(fulldata))
  fulldata$Well<-as.character(fulldata$Well)
  for (i in 1:nrow(deaths)){
    s_death<-deaths[i,]
    s_death$Well<-as.character(s_death$Well)
    temprownum<-rownames(fulldata[fulldata$Hour==s_death[1,c("Hour")] & fulldata$Well == s_death[1,c("Well")] & fulldata$FlyID== s_death[1,c("FlyID")],])
    #print(temprownum)
    tempdeath[as.numeric(temprownum)]<-TRUE
  }
  return(tempdeath)
}


death<-deathdefiner(ex4c_cfu,ex4c_death)
ex4c_cfu<-cbind(ex4c_cfu,death)

dcensor<-ex4c_cfu[ex4c_cfu$Hour==0 & ex4c_cfu$radiance <10000000,c("ID")]
ex4c_cfu_re<-ex4c_cfu[ex4c_cfu$ID==dcensor,]
ex4c_cfu<-ex4c_cfu[ex4c_cfu$ID!=dcensor,]
#function to perform a Ttest between 2 genotypes for n hours 
runningt<-function(data,genocol,sepcol){
  temp_hours<-unique(data[,sepcol])
  temp_geno<-unique(data[,genocol])
  temp_p<-numeric()
  for ( i in 1:length(temp_hours)){
    temp_gh1<-data[data$genotype==temp_geno[1] & data$Hour==temp_hours[i],]
    temp_gh2<-data[data$genotype==temp_geno[2] & data$Hour==temp_hours[i],]
    temptest<-t.test(temp_gh1$radiance, temp_gh2$radiance)
    temp_p<-append(temp_p,temptest$p.value)
  }
  p_out<-cbind(temp_hours,temp_p)
  return(p_out)
}

test_out<-runningt(ex4c_cfu,"genotype","Hour")
fig4b<-merge(ex4c_cfu, test_out, by.x=3, by.y=1 )

fig4b$radiance[fig4b$radiance<=0]<-1

hsub<-c(0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0)
fig4bv1<-fig4b[fig4b$Hour %in% hsub,]
dcensor<-rownames(fig4bv1[fig4bv1$Hour==0 & fig4bv1$radiance <10000000,])

FIG4B<-ggplot(data=fig4bv1, aes(x=as.factor(`Hour`), y=`radiance`,fill=`genotype`) )

FIG4B+
  geom_boxplot(outlier.size = .7, lwd=.3)+
  ggtitle("Figure 4B")+
  ylab("total flux\n (p/s)")+
  xlab("Hour")+
  #scale_shape_manual(values = c(16,13))+
  scale_fill_manual(name="Genotype", values = c("gray","gray40"))+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.6), 
        axis.title.x = element_text(size = 10), 
        axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10, angle = 45), 
        axis.text.y = element_text(size= 10))

#### Figure 4C

# get first death from death data:
ex4c_firstdeath<-data.frame(matrix(nrow = 0, ncol=length(ex4c_death)))
colnames(ex4c_firstdeath)<-colnames(ex4c_death)
for ( i in 1:length(unique(ex4c_death$FlyID))){
  temp_ids<-unique(ex4c_death$FlyID)
  temprows<-ex4c_death[ex4c_death$FlyID==temp_ids[i],]
  ex4c_firstdeath<-rbind(ex4c_firstdeath,temprows[1,])
}

ex4c_firstdeath$ID<-paste(ex4c_firstdeath$Well, ex4c_firstdeath$FlyID, sep="")

firstdeath<-logical()
for ( i in 1:nrow(ex4c_cfu)){
  temp_ID<-ex4c_cfu[i,c("ID")]
  temp_hour<- ex4c_cfu[i,c("Hour")]
  if( nrow(ex4c_firstdeath[ex4c_firstdeath$ID==temp_ID & ex4c_firstdeath$Hour==temp_hour,])==0){
    firstdeath<-append(firstdeath,FALSE)
  } else if(nrow(ex4c_firstdeath[ex4c_firstdeath$ID==temp_ID & ex4c_firstdeath$Hour==temp_hour,])!=0){
    firstdeath<-append(firstdeath,TRUE)
  }
  
  rm(temp_ID,temp_hour)
}
sum(firstdeath)

## [1] 11

ex4c_cfu<-cbind(ex4c_cfu, firstdeath)

death_mean<-mean(ex4c_cfu[ex4c_cfu$death==TRUE & ex4c_cfu$firstdeath==TRUE,c("radiance")])

fig4c<-rbind(ex4c_cfu[ex4c_cfu$death==FALSE & ex4c_cfu$firstdeath==FALSE,], ex4c_cfu[ex4c_cfu$death==TRUE & ex4c_cfu$firstdeath==TRUE,])
ex4c_cfu_ref<-ex4c_cfu_re[1:17,]
firstdeaths<-fig4c[fig4c$firstdeath==T,]
##facet labels:


FIG4c<-ggplot(data = fig4c, aes(x=`Hour`,y=`radiance`, group=`ID`, color=`death`, shape=`death`))
# save 8x4
FIG4c +
  geom_hline(yintercept = death_mean, color = "red")+
  geom_point(size = 2, alpha = 0) +
  ylab("total flux\n (p/s)")+
  xlab("Hour")+
  ggtitle("Figure 4C ")+
  geom_path()+
  scale_color_manual(name="Death" ,values = c("FALSE"="gray20", "TRUE"="red","grey"="grey60"))+
  facet_grid(. ~ genotype)+
  stat_smooth(aes(group = 1), color="royalblue",linetype="dashed")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw()+
  geom_point(size = 2, alpha = 0,data = ex4c_cfu_ref, aes(y=`radiance`,x=`Hour`, color="grey"))+
  geom_point(size = 2, alpha = .5, data= firstdeaths, aes(y=`radiance`,x=`Hour`), color = "red")+
geom_path(data = ex4c_cfu_ref, aes(y=`radiance`,x=`Hour`, color="grey")) + 
  theme(axis.title.x = element_text(size = 10), 
        axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), 
        axis.text.y = element_text(size= 10))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4d

fig4d<-fig4c[fig4c$firstdeath==T,]
FIG4D<-ggplot(fig4d, aes(y=`radiance`))
#save 3x4 reduce width in graphic software
FIG4D +
  geom_histogram(fill='red')+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log', limits = c(1000000,100000000))+
  theme_bw()+
  ggtitle("Figure 4d")+
  theme(axis.title.x = element_text(size = 10), 
        axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), 
        axis.text.y = element_text(size= 10), panel.grid.minor = element_blank())

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 2 rows containing missing values (geom_bar).

Figure S8

ex4c_g90hi<-ex4c_cfu[ex4c_cfu$genotype=="G090" & ex4c_cfu$above_ave==T & ex4c_cfu$Hour==11.5,]
ex4c_g90lo<-ex4c_cfu[ex4c_cfu$genotype=="G090" & ex4c_cfu$above_ave==F & ex4c_cfu$Hour==11.5,]

ex4c_g130hi<-ex4c_cfu[ex4c_cfu$genotype=="G130" & ex4c_cfu$above_ave==T & ex4c_cfu$firstdeath==T ,]
ex4c_g130lo<-ex4c_cfu[ex4c_cfu$genotype=="G130" & ex4c_cfu$above_ave==F & ex4c_cfu$firstdeath==T ,]

t.test(ex4c_g90hi$radiance, ex4c_g90lo$radiance)

## 
##  Welch Two Sample t-test
## 
## data:  ex4c_g90hi$radiance and ex4c_g90lo$radiance
## t = 1.4318, df = 9.3234, p-value = 0.1849
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -260232.6 1170746.9
## sample estimates:
## mean of x mean of y 
##   1435857    980600

t.test(ex4c_g130hi$radiance, ex4c_g130lo$radiance)

## 
##  Welch Two Sample t-test
## 
## data:  ex4c_g130hi$radiance and ex4c_g130lo$radiance
## t = 0.28868, df = 5.9571, p-value = 0.7826
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -10637464  13477464
## sample estimates:
## mean of x mean of y 
##  40120000  38700000

figs8<-rbind(ex4c_g130hi, ex4c_g130lo, ex4c_g90hi, ex4c_g90lo)

FIGS8 <-ggplot(data = figs8, aes(x=`genotype`,y=`radiance`, fill=`above_ave`))

FIGS8 +
  geom_boxplot()+
  ylab("total fluxupon death/end \n (p/s)")+
  xlab("genotype")+
  annotate("text", x="G090", y=3000000,label=paste("n.s."),size=3)+
  annotate("text", x="G130", y=60000000,label=paste("n.s."),size=3)+
  ggtitle("Supplemental Figure 8")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw()+
  scale_fill_discrete("Above Average\nInitial Injection")

Figure 5b

ex4e_cfu<-read.table("Data_table_s12.txt", header = T)
ex4e_diss<-ex4e_cfu # this will be important later
ex4e_death<-read.table("Data_table_s13.txt", header = T)
#Reshapeit!

ex4e_cfu<-gather(ex4e_cfu, "FlyID", "radiance", Fly01:Fly12)
ex4e_death<-gather(ex4e_death, "FlyID", "Death", Fly01:Fly12)
ex4e_death<-ex4e_death[complete.cases(ex4e_death),]
ex4e_death<-ex4e_death[ex4e_death$Death ==T,]
##remove NA

ex4e_cfu$ID<-paste(ex4e_cfu$Well,ex4e_cfu$FlyID, sep="")
ex4e_death$ID<-paste(ex4e_death$Well,ex4e_death$FlyID, sep="")


ex4e_means<-c(mean(ex4e_cfu[ex4e_cfu$genotype=="G090" & ex4e_cfu$Hour==0,c("radiance")]), mean(ex4e_cfu[ex4e_cfu$genotype=="G130" & ex4e_cfu$Hour==0,c("radiance")]))
above_ave<-above_average(ex4e_cfu,ex4e_means)
ex4e_cfu<-cbind(ex4e_cfu, above_ave)

#function to take the death data of ex4e and label all deaths 

deathlabeler<-function(fulldata,deaths){
  tempIDs<-unique(fulldata$ID)
  outdf<-data.frame(matrix(ncol = 10, nrow = 0))
  colnames(outdf)<-c("Well","OD","Dose","genotype","Hour","FlyID","radiance","ID","above_ave","Death")
  for ( i in 1:length(tempIDs)){
    temp_fly<-tempIDs[i]
    temp_data<-fulldata[fulldata$ID==temp_fly,]
    tempdeath<-deaths[deaths$ID==temp_fly,]
    if (nrow(tempdeath)==0 ){
      temp_data$Death<-FALSE
      outdf<-rbind(outdf,temp_data)
    } else{
      firstdeath<-tempdeath[1,]
      #pull out the rownumber of the first death
      deathrow<-which(rownames(temp_data) == rownames(temp_data[temp_data$Hour==firstdeath$Hour,]),)
      Death<-append(rep(FALSE, deathrow-1),rep(TRUE, (nrow(temp_data)-(deathrow-1))))
      temp_data<-cbind(temp_data,Death)
      outdf<-rbind(outdf,temp_data)
    }
  }
  return(outdf)
}
ex4e_labled<-deathlabeler(ex4e_cfu,ex4e_death)

fig5b<-ex4e_labled[ex4e_labled$Hour<=48,]
fig5b$radiance[fig5b$radiance<=0]<-1

d2int<-fig5b[fig5b$radiance<=1000,]

interpolate_data<-function(fulldata){
  full_data<-fulldata
  temp_i<-full_data[full_data$radiance<1000,]
  temp_i<-arrange(temp_i, ID)
  for( i in 1:nrow(temp_i)){
    if (temp_i[i,c("Hour")]<48){
      temphour<-temp_i[i,c("Hour")]
      tempid<-temp_i[i,c("ID")]
      temp_upper<-full_data[full_data$Hour==(temphour-1) & full_data$ID==tempid ,c("radiance")]
      temp_lower<-full_data[full_data$Hour==(temphour+1) & full_data$ID==tempid, c("radiance")]
      if (temp_lower<1000 & temphour+1 != 48){
        temp_lower<-full_data[full_data$Hour==(temphour+2) & full_data$ID==tempid, c("radiance")]
        temp_inter<-((temp_upper-temp_lower)/2)+temp_lower
      } else if (temp_lower<1000 & temphour+1 == 48){
        temp_upper<-full_data[full_data$Hour==(temphour-2) & full_data$ID==tempid ,c("radiance")]
        temp_lower<-full_data[full_data$Hour==(temphour-1) & full_data$ID==tempid, c("radiance")]
        temp_inter<-temp_lower-(temp_upper-temp_lower)
        if ( temp_inter <1000){
          temp_inter<-temp_lower
        }
      } else{
        temp_inter<-((temp_upper-temp_lower)/2)+temp_lower
      }
      full_data[full_data$Hour==temphour & full_data$ID==tempid,c("radiance")]<-temp_inter
    } else if (temp_i[i,c("Hour")]==48){
        temphour<-temp_i[i,c("Hour")]
        tempid<-temp_i[i,c("ID")]
        temp_upper<-full_data[full_data$Hour==(temphour-2) & full_data$ID==tempid ,c("radiance")]
        temp_lower<-full_data[full_data$Hour==(temphour-1) & full_data$ID==tempid, c("radiance")]
        temp_inter<-temp_lower-(temp_upper-temp_lower)
        if ( temp_inter < 0){
          temp_inter<-temp_lower
        }
        full_data[full_data$Hour==temphour & full_data$ID==tempid,c("radiance")]<-temp_inter
        paste("printing else ",i," ",tempid)
    } 
  }
  return(full_data)
}

fig5b<-interpolate_data(fig5b)

fig5c<-fig5b #this will be important later

test_out<-runningt(fig5b,"genotype","Hour")

fig5b<-merge(fig5b,test_out, by.x=5, by.y=1)


hsub<-c(0,3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48)
fig5b<-fig5b[fig5b$Hour %in% hsub,]

test<-ex4e_labled[ex4e_labled$Hour==0,]
test2<-fig5b[fig5b$Hour==0,]

FIG5B<-ggplot(data=fig5b, aes(x=as.factor(`Hour`), y=`radiance`,fill=`genotype`) )
##export 6.8x3.5
FIG5B+
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_boxplot(outlier.size = .7, lwd=.3)+
  #geom_col(position="dodge")+
  ggtitle("Figure 5B")+
  ylab("total flux\n (p/s)")+
  xlab("Hour")+
  #scale_shape_manual(values = c(16,13))+
  scale_fill_manual(name="Genotype", values = c("gray","gray20"))+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  #geom_hline(yintercept = 1000, linetype="dashed", color = "grey20")+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.6), 
        axis.title.x = element_text(size = 10), 
        axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), 
        axis.text.y = element_text(size= 10))

## Warning: Transformation introduced infinite values in continuous y-axis

Figure 6a

fig7a<-fig5c ## this will be important later.
fig5e<-ex4e_cfu

justdeath<-data.frame(matrix(nrow = 0, ncol = length(fig5e)))
colnames(justdeath)<-colnames(fig5e)
for ( i in 1:nrow(ex4e_death)){
  tempdeath <-ex4e_death[i,]
  temp_row<-fig5e[fig5e$ID==tempdeath$ID & fig5e$Hour == tempdeath$Hour,]
  justdeath<-rbind(justdeath,temp_row)
  rm(tempdeath,temp_row)
}
mean_rud<-mean(justdeath$radiance)
## censor deaths 
firstdeath<-logical()
for ( i in 1:nrow(fig5c)){
  temp_ID<-fig5c[i,c("ID")]
  temp_hour<- fig5c[i,c("Hour")]
  if(nrow(ex4e_death[ex4e_death$ID==temp_ID & ex4e_death$Hour==temp_hour,])==0 ){
    firstdeath<-append(firstdeath,FALSE)
  } else if(nrow(ex4e_death[ex4e_death$ID==temp_ID & ex4e_death$Hour==temp_hour,])>0 ){
    firstdeath<-append(firstdeath,TRUE)
  }
  
  rm(temp_ID,temp_hour)
}


fig5c<-cbind(fig5c,firstdeath)
fig5c<-fig5c[fig5c$genotype=="G130",]

live<-fig5c[ fig5c$Death == FALSE & fig5c$firstdeath == FALSE,]
fd<-fig5c[ fig5c$Death == TRUE & fig5c$firstdeath == TRUE,]
alld<-fig5c[ fig5c$Death == TRUE,]
fig5c<-rbind(fig5c[ fig5c$Death == FALSE & fig5c$firstdeath == FALSE,],
                fig5c[ fig5c$Death == TRUE & fig5c$firstdeath == TRUE,])
f6a_firstdeaths<-fig5c[fig5c$firstdeath==T,]

FIG5C <-ggplot(data = fig5c, aes(x=`Hour`,y=`radiance`, group=`ID`, shape=`Death`,color=`Death`))
#export 5x3
FIG5C+
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_hline(yintercept = mean_rud, color = "red")+
  geom_point(size = 2, alpha = 0) +
  ylab("total flux\n (p/s)")+
  xlab("hour")+
  #scale_shape_manual(values = c(16,13))+
  scale_color_manual(name="Death" ,values = c("FALSE"="gray20", "TRUE"="gray20"))+
  ggtitle("Figure 6a ")+
  geom_path()+
  facet_grid(. ~ genotype)+
  stat_smooth(aes(group = 1), color="royalblue",linetype="dashed")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  #geom_hline(yintercept = 1000, linetype="dashed", color = "red")+
  geom_point(size = 2, alpha = .5, data= f6a_firstdeaths, aes(y=`radiance`,x=`Hour`), color = "red")+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10))

## Warning: Transformation introduced infinite values in continuous y-axis

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

figure s10

##function to format object for diss
diss_shape<-function(fulldata,groupcol,rownum){
  groups<-unique(fulldata[,groupcol])
  temp_df<-data.frame(nrow=rownum)
  for (i in 1:length(groups)){
    ##Select a group
    temp_g<-groups[i]
    ##Pull out the group from DF
    temp_data<-fulldata[fulldata[,groupcol]==temp_g ,]
    ##remove group column
    temp_data<-temp_data[,!colnames(temp_data)%in% groupcol]
    ##rename Rows
    rownames(temp_data)<-temp_data$Hour
    temp_data$Hour<-NULL
    #rename columns
    temp_names<-paste(as.character(temp_g),colnames(temp_data), sep = "_")
    colnames(temp_data)<-temp_names
    temp_df<-cbind(temp_df,temp_data)
  }
  temp_df$nrow<-NULL
  return(temp_df)
}
ex4e_diss$OD<-NULL
ex4e_diss$Dose<-NULL
ex4e_130<-ex4e_diss[ex4e_diss$genotype=="G130",]
ex4e_130$genotype <-NULL

ex4e_130<-diss_shape(ex4e_130, "Well",54)
ex4e_130[ex4e_130<=0]<-1
ex4e_130<-log(ex4e_130)

ex4e_130<-ex4e_130[1:49,]
euc130<-diss(ex4e_130, "EUCL")
plot(hclust(euc130), main = "Suppplemental Figure 10\nimd10191 Cluster Dendogram")

Figure 6aii

c1_130<-c("EFly08","GFly01","GFly08")
c2_130<-c("GFly10","HFly10","EFly10","FFly10","HFly08","GFly04","FFly03","HFly06","GFly05","GFly07","HFly12","GFly02","HFly11","GFly03","GFly12","FFly07","HFly02","HFly03","FFly09","EFly02","HFly07","FFly12","GFly11","EFly06","GFly09","EFly04","EFly12","EFly01","GFly06")
c3_130<-c("FFly02","HFly04","FFly06","FFly04","FFly08","HFly05","EFly03","EFly09","FFly01","EFly11","FFly11","EFly05","EFly07")
c4_130<-c("FFly05","HFly01","HFly09")

ex4e_130_re<-rbind(live,alld)

clustlabs<-as.character()
for( i in 1:nrow(ex4e_130_re)){
  temprow<-ex4e_130_re[i,]
  if (temprow$ID %in% c1_130 ){
    clustlabs<-append(clustlabs,"C1")
  } else  if (temprow$ID %in% c2_130){
    clustlabs<-append(clustlabs,"C2")
  } else  if (temprow$ID %in% c3_130){
    clustlabs<-append(clustlabs,"C3")
  } else  if (temprow$ID %in% c4_130){
    clustlabs<-append(clustlabs,"C4")
  }
}

ex4e_130_re<-cbind(ex4e_130_re,clustlabs)

ex4e_130_death<-ex4e_130_re[ex4e_130_re$Death==T,]
ex4e_130_re<-ex4e_130_re[ex4e_130_re$Death==F | ex4e_130_re$firstdeath==T,]
#g130 Plot

#ex4e_130_re$radiance[ex4e_130_re$radiance<=0]<-1
colors_cb<-c("#D81B60","#1E88E5","#FFC107","#004D40")

FIG6Ab<-ggplot(f6a_firstdeaths, aes(y=`radiance`))
# save 3x3 and manually shrink width in graphical program
FIG6Ab +
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_histogram(fill='red')+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log', limits = c(1000,100000000))+
  theme_bw()+
  ggtitle("Figure 6A-ii")+
  theme(axis.title.x = element_text(size = 10), 
        axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), 
        axis.text.y = element_text(size= 10), panel.grid.minor = element_blank())

## Warning: Transformation introduced infinite values in continuous y-axis

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 2 rows containing missing values (geom_bar).

Figure 6b

FIG5D<-ggplot(data=ex4e_130_re, aes(x= `Hour`, y=`radiance`, color=`clustlabs`, group=`ID`, shape=`Death`))
#save 4.5 x 3 + manually Move legends
FIG5D+
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 1, alpha = 0) +
  ylab("total flux\n (p/s)")+
  xlab("Hour")+
  #scale_shape_manual(values = c(16,13))+
  ggtitle("Figure 6B")+
  geom_path()+
  scale_color_manual(name="clustlabs" ,values = colors_cb)+
  theme_bw()+
  facet_wrap(. ~ genotype)+
  #stat_smooth(aes(group = 1), color="black",linetype="dashed")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  geom_point(size = 2, alpha = .5, data= f6a_firstdeaths, aes(y=`radiance`,x=`Hour`), color = "red")+
  geom_path(data=ex4e_130_death, aes(x=`Hour`, y=`radiance`, color=`clustlabs`,group=`ID`),linetype='dashed')+
  #geom_hline(yintercept = 1000, linetype="dashed", color = "red")+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10))

## Warning: Transformation introduced infinite values in continuous y-axis

Figure S9

ex4e_130_hi<-ex4e_130_re[ex4e_130_re$firstdeath==T & ex4e_130_re$above_ave==T,]
ex4e_130_lo<-ex4e_130_re[ex4e_130_re$firstdeath==T & ex4e_130_re$above_ave==F,]

t.test(ex4e_130_hi$radiance,ex4e_130_lo$radiance)

## 
##  Welch Two Sample t-test
## 
## data:  ex4e_130_hi$radiance and ex4e_130_lo$radiance
## t = -0.73327, df = 45.641, p-value = 0.4671
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -8843303  4121446
## sample estimates:
## mean of x mean of y 
##  17113000  19473929

t.test(ex4e_130_hi$Hour,ex4e_130_lo$Hour)

## 
##  Welch Two Sample t-test
## 
## data:  ex4e_130_hi$Hour and ex4e_130_lo$Hour
## t = -0.65484, df = 33.406, p-value = 0.5171
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.691967  2.406253
## sample estimates:
## mean of x mean of y 
##  31.00000  32.14286

figs9<-rbind(ex4e_130_hi,ex4e_130_lo)

FIGS9 <-ggplot(data = figs9, aes(x=`genotype`,y=`radiance`, fill=`above_ave`))
#FIGS9 <-ggplot(data = figs9, aes(x=`genotype`,y=`Hour`, fill=`above_ave`))

FIGS9 +
  geom_boxplot()+
  ylab("total fluxupon death \n (p/s)")+
  xlab("genotype")+
  annotate("text", x="G130", y=90000000,label=paste("n.s."),size=3)+
  ggtitle("Supplemental Figure 9")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log',limits = c(1000000,100000000))+
  theme_bw()+
  scale_fill_discrete("Above Average\nInitial Injection")

####Figure S11

nrow(ex4e_130_re[ex4e_130_re$Hour==0 & ex4e_130_re$clustlabs=="C1" ,])

## [1] 3

nrow(ex4e_130_re[ex4e_130_re$Hour==0 & ex4e_130_re$clustlabs=="C2" ,])

## [1] 29

nrow(ex4e_130_re[ex4e_130_re$Hour==0 & ex4e_130_re$clustlabs=="C3" ,])

## [1] 13

nrow(ex4e_130_re[ex4e_130_re$Hour==0 & ex4e_130_re$clustlabs=="C4" ,])

## [1] 3

tdeath<-ex4e_130_re[ex4e_130_re$firstdeath==T,]
tdeath<-tdeath[! rownames(tdeath) %in% c(1959,2392,2823),]
tc1<-tdeath[tdeath$clustlabs=="C1",]
tc2<-tdeath[tdeath$clustlabs=="C2",]
tc3<-tdeath[tdeath$clustlabs=="C3",]
tc4<-tdeath[tdeath$clustlabs=="C4",]

t.test(tc1$Hour,tc2$Hour)

## 
##  Welch Two Sample t-test
## 
## data:  tc1$Hour and tc2$Hour
## t = -0.3127, df = 2.4072, p-value = 0.7796
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -11.140572   9.393445
## sample estimates:
## mean of x mean of y 
##  29.33333  30.20690

t.test(tc1$Hour,tc3$Hour)

## 
##  Welch Two Sample t-test
## 
## data:  tc1$Hour and tc3$Hour
## t = -1.5161, df = 3.9256, p-value = 0.2054
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -13.714571   4.073546
## sample estimates:
## mean of x mean of y 
##  29.33333  34.15385

t.test(tc1$Hour,tc4$Hour)

## 
##  Welch Two Sample t-test
## 
## data:  tc1$Hour and tc4$Hour
## t = -1.3416, df = 2.9412, p-value = 0.2739
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -27.19297  11.19297
## sample estimates:
## mean of x mean of y 
##  29.33333  37.33333

t.test(tc2$Hour,tc3$Hour)

## 
##  Welch Two Sample t-test
## 
## data:  tc2$Hour and tc3$Hour
## t = -2.0544, df = 17.782, p-value = 0.05494
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.98682817  0.09292896
## sample estimates:
## mean of x mean of y 
##  30.20690  34.15385

t.test(tc2$Hour,tc4$Hour)

## 
##  Welch Two Sample t-test
## 
## data:  tc2$Hour and tc4$Hour
## t = -1.3202, df = 2.0986, p-value = 0.3123
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -29.33720  15.08432
## sample estimates:
## mean of x mean of y 
##  30.20690  37.33333

t.test(tc3$Hour,tc4$Hour)

## 
##  Welch Two Sample t-test
## 
## data:  tc3$Hour and tc4$Hour
## t = -0.56702, df = 2.4393, p-value = 0.6186
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -23.58527  17.22630
## sample estimates:
## mean of x mean of y 
##  34.15385  37.33333

figs11<-rbind(tc1,tc2,tc3,tc4)

FIGS11 <-ggplot(data = figs11, aes(x=`clustlabs`,y=`Hour`))

FIGS11 +
  geom_boxplot()+
  #geom_point(size=2, alpha=.5, color = "blue" )+
  geom_jitter(width = .2, height = 0, color = "blue",size=2, alpha=.5)+
  ylab("hour of death")+
  xlab("cluster")+
  ggtitle("Supplemental Figure 11")+
  ylim(20,50)+
  theme_bw()+
  scale_fill_discrete("Above Average\nInitial Injection")

tdeath$count<-1
FIG5C<-ggplot(data = tdeath, aes(x=as.factor(`Hour`)))

FIG5C + 
  geom_bar()+
  xlab("time of death\n(hour)")+
  ggtitle("Figure 5C")+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10),
        panel.grid = element_blank())

## Srikis code

#Gonna go with spearman correltions because they with with both liniear and mopnotonic stuff.
inscorr<-read.csv("Data_table_14.csv", header = T)

tde24<-ex4e_130_re[ex4e_130_re$firstdeath==T & ex4e_130_re$Hour==24,c("ID")]
tde28<-ex4e_130_re[ex4e_130_re$firstdeath==T & ex4e_130_re$Hour==28,c("ID")]
tde32<-ex4e_130_re[ex4e_130_re$firstdeath==T & ex4e_130_re$Hour==32,c("ID")]
tde48<-ex4e_130_re[ex4e_130_re$firstdeath==T & ex4e_130_re$Hour==48,c("ID")]

tde_labs<-numeric()
for ( i in 1:nrow(ex4e_130_re)){
  tempid<-ex4e_130_re[i,c("ID")]
  if (tempid %in% tde24){
    tde_labs<-append( tde_labs, 24)
  } else if (tempid %in% tde28){
    tde_labs<-append( tde_labs, 28)
  } else if (tempid %in% tde32){
    tde_labs<-append( tde_labs, 32)
  } else if (tempid %in% tde48){
    tde_labs<-append( tde_labs, 48)
  }
}

ex4e_130_re<- cbind(ex4e_130_re, tde_labs )

in_load<-ex4e_130_re[ex4e_130_re$Hour==0,]

FIG_6c<-ggplot( data= in_load, aes( x=as.factor(`tde_labs`), y=`radiance`))
FIG_6c+
  geom_boxplot(outlier.size = 0, outlier.stroke = 0)+
  geom_jitter(width = .2)+
  theme_bw()+
  ggtitle("Fig6C")+
  ylab( "initial flux\n(p/s)")+
  xlab( "Time of Death (hour)")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log', limits = c(9000,1000000))+
  annotate("text", x=2, y=900000,label=paste("ρ=-0.29 ,CI:[-0.56,0.00]"),size=3)+
  theme(plot.title = element_text(hjust = 0.6), 
        axis.title.x = element_text(size = 10), 
        axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10, angle = 45), 
        axis.text.y = element_text(size= 10))

h20_load<-ex4e_130_re[ex4e_130_re$Hour==20,]
FIG_6d<-ggplot( data= h20_load, aes( x=as.factor(`tde_labs`), y=`radiance`))
FIG_6d+
  geom_boxplot(outlier.size = 0, outlier.stroke = 0)+
  geom_jitter(width = .2)+
  theme_bw()+
  ggtitle("Fig6d")+
  ylab( "flux 20h\n(p/s)")+
  xlab( "time of death (hour)")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log', limits = c(100000,100000000))+
  annotate("text", x=2, y=90000000,label=paste("ρ=-0.61 ,CI:[-0.78,-0.39]"),size=3)+
  theme(plot.title = element_text(hjust = 0.6), 
        axis.title.x = element_text(size = 10), 
        axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10, angle = 45), 
        axis.text.y = element_text(size= 10))

FIG_6e<-ggplot( data= inscorr, aes( x=as.factor(`upper_TTD`), y=`log10_auc_cfu_20h` ))
FIG_6e+
  geom_boxplot(outlier.size = 0, outlier.stroke = 0)+
  geom_jitter(width = .2)+
  theme_bw()+
  ggtitle("Fig6d")+
  ylim(5,6.75)+
  ylab( "log(AUC) 20h")+
  xlab( "time of death (hour)")+
  annotate("text", x=2, y=6.7,label=paste("ρ=-0.59 ,CI:[-0.75,-0.36]"),size=3)+
  theme(plot.title = element_text(hjust = 0.6), 
        axis.title.x = element_text(size = 10), 
        axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10, angle = 45), 
        axis.text.y = element_text(size= 10))

Figure 7a

fig7a<-fig7a[fig7a$genotype=="G090",]
#fig7a<-interpolate_data(fig7a)

FIG7a <-ggplot(data = fig7a, aes(x=`Hour`,y=`radiance`, group=`ID`, shape=`Death`,color=`Death`))
#save 5x3.2
FIG7a+
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 2, alpha = 0) +
  ylab("total flux\n (p/s)")+
  xlab("Hour")+
  #scale_color_manual(name="Death" ,values = c("FALSE"="#00CCCC", "TRUE"="red"))+
  ggtitle("Figure 7a ")+
  geom_path(color="gray20")+
  facet_grid(. ~ genotype)+
  stat_smooth(aes(group = 1), color="royalblue",linetype="dashed")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  #geom_hline(yintercept = 1000, linetype="dashed", color = "red")+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10))

## Warning: Transformation introduced infinite values in continuous y-axis

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

####Figure S12

ex4e_90<-ex4e_diss[ex4e_diss$genotype=="G090",]
ex4e_90$genotype <-NULL

ex4e_90<-diss_shape(ex4e_90, "Well",54)

interleaf_data<-function(full_data, interpolated_data,interlist){
  interpolated_data$nID<-paste0(interpolated_data$Well,"_",interpolated_data$FlyID)
  interlist$nID<-paste0(interlist$Well,"_",interlist$FlyID)
  for ( i in 1:nrow(interlist)){
    temp_id<-interlist[i,c("nID")]
    temp_hour<-interlist[i,c("Hour")]
    new_rad<-interpolated_data[interpolated_data$nID==temp_id & interpolated_data$Hour==temp_hour ,c("radiance")]
    full_data[rownames(full_data) == temp_hour, c(temp_id)]<-new_rad
  }
  return(full_data)
}
ex4e_90<-interleaf_data(ex4e_90,fig7a,d2int)

ex4e_90<-ex4e_90[1:49,]
ex4e_90<-log(ex4e_90)


###

euc90<-diss(ex4e_90, "EUCL")

plot(hclust(euc90), main = "Suppplemental Figure 12A\nWT Cluster Dendogram")

#clust try 1 ( based on tree clusters)
c1_90<-c("CFly06","CFly07","DFly08","AFly12","DFly11")
c2_90<-c("AFly03","DFly09","BFly06","CFly05","CFly08","AFly04", "AFly07")
c3_90<-c("BFly01","CFly12","BFly04","BFly05","CFly04","AFly05","BFly02","BFly10","BFly08","BFly12", "BFly07","DFly10","AFly01","CFly02")
c4_90<-c("AFly10","DFly01","DFly07","AFly09","DFly05","AFly02","AFly11", "AFly06", "AFly08","BFly03",    "BFly09",  "BFly11",  "CFly01",  "CFly03","CFly09", "CFly10", "CFly11","DFly02", "DFly03", "DFly04",  "DFly06","DFly12")



ex4e_90_re<-fig7a
clustlabs<-as.character()
for( i in 1:nrow(ex4e_90_re)){
  temprow<-ex4e_90_re[i,]
  if (temprow$ID %in% c1_90 ){
    clustlabs<-append(clustlabs,"C1")
  } else  if (temprow$ID %in% c2_90){
    clustlabs<-append(clustlabs,"C2")
  } else  if (temprow$ID %in% c3_90){
    clustlabs<-append(clustlabs,"C3")
  }  else  if (temprow$ID %in% c4_90){
    clustlabs<-append(clustlabs,"C4")
  } 
}
ex4e_90_re<-cbind(ex4e_90_re,clustlabs)
##g90

FIG_S12B<-ggplot(data=ex4e_90_re, aes(x= `Hour`, y=`radiance`, color=`clustlabs`, group=`ID`))
FIG_S12B+
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 1, alpha = 0) +
  ylab("total flux\n (p/s)")+
  xlab("hour")+
  ggtitle("Supplemental Figure 12b\n wt unsupervised clustering  ")+
  geom_path()+
  scale_color_manual(name="clustlabs" ,values = colors_cb)+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10))

## Warning: Transformation introduced infinite values in continuous y-axis

##90 short
ex4e_90short<-ex4e_90[1:31,]
euc90short<-diss(ex4e_90short, "EUCL")
plot(hclust(euc90short), main = "Suppplemental Figure 12C\nWT Cluster Dendogram")

c1_90short<-c("BFly07", "DFly07", "DFly02", "DFly12", "BFly01", "CFly12", "AFly05", "CFly04", "BFly02", "BFly08", "BFly10", "BFly04", "BFly12", "BFly09", "BFly05", "CFly02", "AFly01" )
c2_90short<-c("AFly11", "AFly06", "BFly11", "CFly01", "CFly03", "CFly09", "CFly10", "CFly11", "DFly04", "DFly06", "DFly10", "AFly10", "DFly01",  "AFly09", "DFly05", "AFly02", "CFly07", "CFly06", "BFly03", "DFly03")
c3_90short<-c("AFly12", "DFly08", "DFly11", "AFly04","AFly07", "AFly08", "BFly06", "CFly05", "DFly09", "AFly03", "CFly08")

ex4e_90_reshort<-fig7a[fig7a$genotype=="G090",]
clustlabs<-as.character()
for( i in 1:nrow(ex4e_90_reshort)){
  temprow<-ex4e_90_reshort[i,]
  if (temprow$ID %in% c1_90short ){
    clustlabs<-append(clustlabs,"C1")
  } else  if (temprow$ID %in% c2_90short){
    clustlabs<-append(clustlabs,"C2")
#clusts 3 + 4 only includded in try 1 for this data
  } else  if (temprow$ID %in% c3_90short){
    clustlabs<-append(clustlabs,"C3")
  } 
}
ex4e_90_reshort<-cbind(ex4e_90_reshort,clustlabs)
###
FIG_S12D<-ggplot(data=ex4e_90_reshort, aes(x= `Hour`, y=`radiance`, color=`clustlabs`, group=`ID`))

FIG_S12D+
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 1, alpha = 0) +
  ylab("total flux\n (p/s)")+
  xlab("Hour")+
  scale_color_manual(name="clustlabs" ,values = colors_cb)+
  ggtitle("Supplemental Figure 12D\nWT <h30 clustering  ")+
  geom_path()+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10))

## Warning: Transformation introduced infinite values in continuous y-axis

Figure 7b

#picked by hand

c1_90hand<-c("AFly02","AFly12","DFly11", "AFly07", "CFly07", "DFly08", "CFly06", "BFly03","AFly04", "DFly03")
c2_90hand<-c("BFly10","AFly11","BFly05","BFly02","CFly12","AFly01", "AFly06", "AFly08", "BFly07", "BFly08", "BFly09", "BFly11", "BFly12", "CFly01", "CFly03", "CFly05",  "CFly08", "CFly09", "CFly10", "CFly11", "DFly02",  "DFly04", "DFly06",  "DFly09", "DFly10", "DFly12",  "AFly05",   "AFly03", "AFly10", "BFly01", "BFly04", "DFly01", "DFly07", "AFly09", "DFly05", "BFly06","CFly02","CFly04")

ex4e_90_rehand<-fig7a[fig7a$genotype=="G090",]
clustlabs<-as.character()
for( i in 1:nrow(ex4e_90_rehand)){
  temprow<-ex4e_90_rehand[i,]
  if (temprow$ID %in% c1_90hand ){
    clustlabs<-append(clustlabs,"C1")
  } else  if (temprow$ID %in% c2_90hand){
    clustlabs<-append(clustlabs,"C2")
  } 
}

ex4e_90_rehand<-cbind(ex4e_90_rehand,clustlabs)

ex4e_90_rehand$radiance[ex4e_90_rehand$radiance<=0]<-1

FIG7b<-ggplot(data=ex4e_90_rehand, aes(x= `Hour`, y=`radiance`, color=`clustlabs`, group=`ID`))

FIG7b+
  annotate("rect", xmin = -Inf, xmax =Inf, ymin =0, ymax = 1000, fill = "gray", alpha = .4, color = NA)+
  geom_point(size = 1, alpha = 0) +
  ylab("total flux\n (p/s)")+
  xlab("Hour")+
  ggtitle("Figure 7b")+
  scale_color_manual(name="clustlabs" ,values = colors_cb)+
  geom_path()+
  facet_wrap(. ~ genotype)+
  stat_smooth(aes(group = 1), color="black",linetype="dashed")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log')+
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5), 
        axis.title.x = element_text(size = 10), axis.title.y = element_text(size = 10),
        axis.text.x = element_text(size= 10), axis.text.y = element_text(size= 10))

## Warning: Transformation introduced infinite values in continuous y-axis

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

figure S13

ex4e_90_hi<-ex4e_90_rehand[ ex4e_90_rehand$above_ave==T & ex4e_90_rehand$Hour==48,]
ex4e_90_lo<-ex4e_90_rehand[ ex4e_90_rehand$above_ave==F & ex4e_90_rehand$Hour==48,]
t.test(ex4e_90_hi$radiance, ex4e_90_lo$radiance)

## 
##  Welch Two Sample t-test
## 
## data:  ex4e_90_hi$radiance and ex4e_90_lo$radiance
## t = -1.6621, df = 44.725, p-value = 0.1035
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -11944.871   1144.712
## sample estimates:
## mean of x mean of y 
##  11680.48  17080.56

figs13a<-rbind(ex4e_90_hi, ex4e_90_lo)

FIGS13A <-ggplot(data = figs13a, aes(x=`above_ave`, y=`radiance`))

FIGS13A +
  geom_boxplot()+
  geom_point( alpha=.7, size=2, position = "jitter", color = "blue")+
  ylab("final radiance")+
  xlab("above average injection load")+
  annotate("text", x=1.5, y=100000,label=paste("n.s."))+
  ggtitle("Supplemental Figure 13A")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log', limits=c( 1000,100000))+
  theme_bw()+
  scale_fill_discrete("Above Average\nInitial Injection")

ex4e_90c1<-ex4e_90_rehand[ ex4e_90_rehand$clustlabs=="C1" & ex4e_90_rehand$Hour==48,]
ex4e_90c2<-ex4e_90_rehand[ ex4e_90_rehand$clustlabs=="C2" & ex4e_90_rehand$Hour==48,]

t.test(ex4e_90c1$radiance, ex4e_90c2$radiance)

## 
##  Welch Two Sample t-test
## 
## data:  ex4e_90c1$radiance and ex4e_90c2$radiance
## t = 0.77766, df = 10.499, p-value = 0.4539
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -8461.206 17624.100
## sample estimates:
## mean of x mean of y 
##  18345.00  13763.55

figs13b<-rbind(ex4e_90c1, ex4e_90c2)

FIGS13B <-ggplot(data = figs13b, aes(x=`clustlabs`, y=`radiance`))

FIGS13B +
  geom_boxplot()+
  geom_point( alpha=.7, size=2, position = "jitter", color = "blue")+
  ylab("final radiance")+
  xlab("cluster")+
  annotate("text", x=1.5, y=100000,label=paste("n.s."))+
  ggtitle("Supplemental Figure 13B")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans='log',limits=c( 1000,100000))+
  theme_bw()+
  scale_fill_discrete("Above Average\nInitial Injection")

figure s14

tecan data

ivt<-read.table("Data_table_s15.txt", header = T)
## what is teh background noise?
tecan_back<-mean(ivt[ivt$Type=="food",c("TECAN")])

flies<-ivt[ivt$Type=="Fly",]
flies<-flies[complete.cases(flies),]

IVT_plot<-ggplot(data=flies, aes( x = `TECAN`, `IVS`, color=as.factor(`OD`)))
IVT_plot+
  geom_point()+
  ylab("IVIS\n(p/s)")+
  xlab("TECAN\n(counts/s)")+
  scale_color_discrete(name="OD",labels=c(expression("6x10"^-5),expression("6x10"^-4),"0.006","0.06","0.6","6" ))+
  scale_x_continuous(breaks= ax_breaks,labels=ax_labeles,trans="log")+
  scale_y_continuous(breaks= ax_breaks,labels=ax_labeles,trans="log")+
  geom_hline(yintercept=1000,linetype="dashed", color = "red")+
  geom_vline(xintercept=100,linetype="dashed", color = "blue" )

BLI Data

Bryan Ramirez-Corona

10/13/2020

Summary

Libraries

Determining ilux CFUs/µl at OD1

Figure S1

characterizing the relationship between radiance and CFUs

Looking in liquid culture

Figure 2A

looking in injected flies

Figure S3

Looking at our ability to distinguish/track infections over time

Figure 3b

Figure 3c

Figure S5

Looking at our ability to track different types for infections

Figure 4b

Figure 4d

Figure S8

Figure 5b

Figure 6a

figure s10

Figure 6aii

Figure 6b

Figure S9

Figure 7a

Figure 7b

figure S13

figure s14